Mechanistic-Based Reliability Evaluation of FC-PBGA Under Thermal and Flexural Loading
Bibliographic record
Abstract
ABSTRACT Thermal and mechanical behavior of a flip chip plastic ball grid array (FC-PBGA) assembly may be investigated with analytical methods. This paper presents new analytical solutions for the evaluation of thermal warpage, mechanical deflection and interfacial stresses-strains in adhesively bonded trilayer structures including assemblies and its constituents such as the IC (Integrated Circuit) chip, substrate laminate, printed circuit board (PCB), underfill layer and board level solder joints. It is a challenging task to obtain internal interfacial stresses-strains in a package experimentally, yet these parameters are essential to the understanding and improvement of the reliability of individual layers and the entire package. The new analytical solutions are developed with less restriction as compared with those of the existing analytical models, thus giving enhanced accuracy to such evaluation if the materials thermal and mechanical properties are known and certain. Furthermore, this paper presents a novel hybrid experimental-analytical inverse method (HEAIM) that is formulated based on the above analytical structure models. With experimentally measured global structural deformation of a package subjected to a thermal cycle, HEAIM is capable of inversely evaluate and characterize the thermal properties and the temperature dependent constitutive behavior for individual layers of the package. Both analytical model and inverse method as proposed can provide pertinent guidance to design for reliability of complex layered structures at different levels of packaging, such as the modules, substrates, PCBs and board-level assemblies, etc.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".